In this paper, we propose a recursive identification technique for nonlinear discrete-time multivariable dynamical systems. Extending an early result to multivariable systems [8], the technique approaches a nonlinear system identification problem in two stages: One is to build up recursively a RBF (Radial-Basis-Function) neural net model structure including the size of the neural net and the parameters in the RBF neurons; the other is to design a stable recursive weight updating algorithm to obtain the weights of the net in an efficient way.